Quantitative information on how well a horse clears a jump has great potential to support the rider in improving the horse’s jumping performance. This study investigated the validation of a GPS-based inertial measurement unit, namely Alogo Move Pro, compared with a traditional optical motion capture system. Accuracy and precision of the three jumping characteristics of maximum height (Zmax), stride/jump length (lhorz), and mean horizontal speed (vhorz) were compared. Eleven horse–rider pairs repeated two identical jumps (an upright and an oxer fence) several times (n = 6 to 10) at different heights in a 20 × 60 m tent arena. The ground was a fiber sand surface. The 24 OMC (Oqus 7+, Qualisys) cameras were rigged on aluminum rails suspended 3 m above the ground. The Alogo sensor was placed in a pocket on the protective plate of the saddle girth. Reflective markers placed on and around the Alogo sensor were used to define a rigid body for kinematic analysis. The Alogo sensor data were collected and processed using the Alogo proprietary software; stride-matched OMC data were collected using Qualisys Track Manager and post-processed in Python. Residual analysis and Bland–Altman plots were performed in Python. The Alogo sensor provided measures with relative accuracy in the range of 10.5–20.7% for stride segments and 5.5–29.2% for jump segments. Regarding relative precision, we obtained values in the range of 6.3–14.5% for stride segments and 2.8–18.2% for jump segments. These accuracy differences were deemed good under field study conditions where GPS signal strength might have been suboptimal.

Accurate localization is crucial for numerous applications. While several methods exist for outdoor localization, typically relying on GPS signals, these approaches become unreliable in environments subject to a weak GPS signal or GPS outage. Many researchers have attempted to address this limitation, primarily focusing on real-time solutions. However, for applications that do not require real-time localization, these methods remain suboptimal. This paper presents a novel Transformer-based bidirectional encoder approach to address, in postprocessing, the localization challenges during GPS weak signal phases or GPS outages. Our method predicts the velocity during periods of weak or lost GPS signals and calculates the position through bidirectional velocity integration. Additionally, it incorporates position interpolation to ensure smooth transitions between active GPS and GPS outage phases. Applied to a dataset tracking horse positions—which features velocities up to 10 times those of pedestrians and higher acceleration—our approach achieved an average trajectory error below 3 m, while maintaining stable relative distance errors regardless of the GPS outage duration.

Lameness and gait irregularities are significant concerns in equine health management, affecting performance, welfare, and economic value. Traditional observational methods rely on subjective expert assessments, which can lead to inconsistencies in detecting subtle or early-stage lameness. While AI-based approaches have emerged, many require multiple sensors, force plates, or video systems, making them costly and impractical for field deployment. In this applied research study, we present a stride-level classification system that utilizes a single inertial measurement unit (IMU) and a one-dimensional convolutional neural network (1D CNN) to objectively differentiate between sound and lame horses, with a primary focus on the trot gait. The proposed system was tested under real-world conditions, achieving a 90% session-level accuracy with no false positives, demonstrating its robustness for practical applications. By employing a single, non-intrusive, and readily available sensor, our approach significantly reduces the complexity and cost of hardware requirements while maintaining high classification performance. These results highlight the potential of our CNN-based method as a field-tested, scalable solution for automated lameness detection. By enabling early diagnosis, this system offers a valuable tool for preventing minor gait irregularities from developing into severe conditions, ultimately contributing to improved equine welfare and performance in veterinary and equestrian practice.

Accurate localization is crucial for numerous applications. While several methods exist for out-door localization, typically relying on GPS signals, these approaches become unreliable in indoor environments due to weak GPS signal or GPS outage. Many researchers have attempted to ad-dress this limitation, primarily focusing on real-time solutions. However, for applications that do not require real-time localization, these methods remain suboptimal. This paper presents a novel Transformer-based bidirectional encoder approach to address indoor localization challenges in postprocessing. Our method predicts velocity during periods of weak or lost GPS signal and calculates position through bidirectional velocity integration. Additionally, it incorporates posi-tion interpolation to ensure smooth transitions between active GPS and GPS outage phases. Applied to a dataset tracking horse positions—which features velocities up to 10 times those of pedestrians and higher acceleration—our approach achieved an average trajectory error below 3 meters, while maintaining stable relative distance errors regardless of GPS outages duration.